The importance of enterprise data management has increased significantly in recent times. Currently, systems lack the intelligence to detect data errors that do not conform to business rules or other rules and the ability to regulate data quality—it relies heavily on human intervention to identify business rules. As a result, bad data in a system can negatively impact enterprise applications or any business processes that utilize the data stored in the system.
Currently, there are tools that perform rudimentary profiling of data, providing out-of-the-box outputs like minimum, maximum, medium, average, most frequent word, field pattern, and other such data quality parameters. However, these tools do not come with out-of-the-box business rules. Such rules have to be provided by the business users. A common issue may be that the business has not documented these rules or the personnel knowledgeable about such rules may not have documented the rules. The existing data tools in the market depend on an end user to analyze and provide the mapping between source and target systems. This process of providing business rules to profile data depends on a manual analysis of data fields and can be time consuming when there are multiple sources/databases as part of a complex system landscape. This task may be more challenging when the source is a manual spreadsheet or unstructured data. Similar challenges may be encountered in profiling data to be migrated from one system to another. Presently, a user may rely heavily on the business to provide the correct value of a data or to identify an issue with the data.
Bad/low quality data may compromise the accuracy of the output of systems. For example, if the data quality of addresses stored in the production database system is low, then addresses may be incorrect or may be rendered unusable for accurately determining delivery locations, calculating taxes based on location, and performing other such functional activities. Furthermore, each of the analytical tools applied to the series of data may suppress any critical data for the purpose of analysis, thereby leading to a high probability of missing critical information and generating inaccurate conclusions and inferences. Furthermore, such methods may not consider real-time factors while generating inferences. Unfortunately, organizations operate in an “always on” environment, thereby making the process of analysis and data management using such methods difficult and inaccurate.
There is therefore a requirement for an intelligent data harmonization model, that may detect data patterns from various data sources and automatically generate insights that may be converted into business rules that could be used to check data quality. There is also a requirement for an intelligent data harmonization model, that may consider future factors, and complex organizational scenarios along with taking into account real-time factors for the generation of specific modeling details, thereby assisting with real-time decisions. Furthermore, there is a requirement for a data harmonization model, which can evolve continuously based on a changing data paradigm.
Accordingly, a technical problem with the currently available processes that ensure data quality is that they may be inefficient, inaccurate, and/or not scalable. These data quality processes may also rely heavily on the business user to perform manual tasks during the mapping, profiling and cleansing activities. There is therefore a requirement for an artificial intelligence (AI)-based data quality system, which may perform AI and machine learning based data profiling, data mapping, and data cleansing.